Large language models (LLMs) are increasingly used as virtual survey respondents, yet most evaluations compare only item-level marginal response distributions. Such evaluations can miss two failures central to computational social science: synthetic respondents may distort correlations among attitudes, and errors may be distributed unevenly across countries or demographic groups. We introduce FairSynth, an instrument-relative framework for auditing and calibrating closed-ended, answer-only LLM survey simulations against existing anchor surveys. FairSynth evaluates marginal fidelity, Structural Attitude Fidelity (item–item correlation similarity), group representation error, distributional flattening, and between-group sign reversals. It then applies a model-agnostic post-hoc logit-shift layer with shared group parameters to reduce disparities in representation error while preserving attitude structure. Experiments on World Values Survey Wave 7 and General Social Survey 2022 show that FairSynth reduces worst-group representation error and sign reversals while improving structural fidelity on the evaluated questionnaires. FairSynth is not a substitute for human surveys; it is a measurement-fairness toolkit for auditing, stress-testing, and calibrating synthetic survey simulations when anchor data are available.